Human Performance Final Report
Jared Smith
2023-05-07
library(readr)
library(ggplot2)
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggdark)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(ggpubr)
library(xgboost)##
## Attaching package: 'xgboost'## The following object is masked from 'package:plotly':
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## slice## The following object is masked from 'package:dplyr':
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## slicelibrary(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:plotly':
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## select## The following object is masked from 'package:dplyr':
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## selectlibrary(caret)## Loading required package: latticelibrary(corrplot)## corrplot 0.92 loadedlibrary(ggExtra) # Load ggExtra
library(xgboost) # Load XGBoost
source("a_insights_shap_functions.r") # Load SHAP functions
library(Metrics) # Load metrics##
## Attaching package: 'Metrics'## The following objects are masked from 'package:caret':
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## precision, recalllibrary(pROC) # Load proc ## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following object is masked from 'package:Metrics':
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## auc## The following objects are masked from 'package:stats':
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## cov, smooth, varday <- read_csv("day_approach_maskedID_timeseries.csv")## Rows: 42766 Columns: 73## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (73): nr. sessions, total km, km Z3-4, km Z5-T1-T2, km sprinting, streng...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.week <- read_csv("week_approach_maskedID_timeseries.csv")## Rows: 42798 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (72): nr. sessions, nr. rest days, total kms, max km one day, total km Z...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Remove rows with missing or invalid values in injury column
week <- week[complete.cases(week$injury), ]summary(week)## nr. sessions nr. rest days total kms max km one day
## Min. : 0.000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 5.000 1st Qu.:1.000 1st Qu.: 22.80 1st Qu.: 9.00
## Median : 6.000 Median :1.000 Median : 44.80 Median : 13.40
## Mean : 5.809 Mean :1.875 Mean : 49.54 Mean : 14.01
## 3rd Qu.: 7.000 3rd Qu.:3.000 3rd Qu.: 70.10 3rd Qu.: 18.30
## Max. :14.000 Max. :7.000 Max. :242.00 Max. :131.00
## total km Z3-Z4-Z5-T1-T2 nr. tough sessions (effort in Z5, T1 or T2)
## Min. : 0.000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000
## Median : 8.000 Median :1.0000
## Mean : 9.434 Mean :0.9302
## 3rd Qu.: 14.600 3rd Qu.:2.0000
## Max. :100.000 Max. :6.0000
## nr. days with interval session total km Z3-4 max km Z3-4 one day
## Min. :0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median :2.000 Median : 0.000 Median : 0.000
## Mean :1.673 Mean : 4.859 Mean : 3.457
## 3rd Qu.:3.000 3rd Qu.: 8.000 3rd Qu.: 6.300
## Max. :7.000 Max. :79.800 Max. :75.000
## total km Z5-T1-T2 max km Z5-T1-T2 one day total hours alternative training
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.500 Median : 1.200 Median : 0.000
## Mean : 4.064 Mean : 2.725 Mean : 1.149
## 3rd Qu.: 6.300 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :80.000 Max. :76.000 Max. :52.500
## nr. strength trainings avg exertion min exertion max exertion
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8156 Mean :0.3199 Mean :0.1887 Mean :0.4706
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :0.9800 Max. :0.9800 Max. :1.0000
## avg training success min training success max training success
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3700 Median :0.7300
## Mean :0.4475 Mean :0.3384 Mean :0.5251
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery min recovery max recovery nr. sessions.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2555 Mean :0.1856 Mean :0.3442 Mean : 5.808
## 3rd Qu.:0.3600 3rd Qu.:0.2500 3rd Qu.:0.5200 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.1 total kms.1 max km one day.1 total km Z3-Z4-Z5-T1-T2.1
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 22.20 1st Qu.: 8.80 1st Qu.: 0.800
## Median :1.000 Median : 44.40 Median : 13.30 Median : 8.000
## Mean :1.879 Mean : 49.26 Mean : 13.92 Mean : 9.384
## 3rd Qu.:3.000 3rd Qu.: 70.00 3rd Qu.: 18.30 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :130.00 Max. :106.200
## nr. tough sessions (effort in Z5, T1 or T2).1 nr. days with interval session.1
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9247 Mean :1.664
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.1 max km Z3-4 one day.1 total km Z5-T1-T2.1
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.400
## Mean : 4.841 Mean : 3.438 Mean : 4.022
## 3rd Qu.: 8.000 3rd Qu.: 6.300 3rd Qu.: 6.200
## Max. :85.000 Max. :75.000 Max. :80.000
## max km Z5-T1-T2 one day.1 total hours alternative training.1
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.693 Mean : 1.172
## 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :76.000 Max. :52.500
## nr. strength trainings.1 avg exertion.1 min exertion.1 max exertion.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8182 Mean :0.3196 Mean :0.1881 Mean :0.4701
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg training success.1 min training success.1 max training success.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3800 Median :0.7300
## Mean :0.4481 Mean :0.3398 Mean :0.5254
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.1 min recovery.1 max recovery.1 nr. sessions.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2551 Mean :0.1851 Mean :0.3436 Mean : 5.811
## 3rd Qu.:0.3600 3rd Qu.:0.2500 3rd Qu.:0.5200 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.2 total kms.2 max km one day.2 total km Z3-Z4-Z5-T1-T2.2
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 21.70 1st Qu.: 8.60 1st Qu.: 0.200
## Median :1.000 Median : 43.90 Median : 13.20 Median : 7.900
## Mean :1.884 Mean : 48.81 Mean : 13.82 Mean : 9.297
## 3rd Qu.:3.000 3rd Qu.: 69.60 3rd Qu.: 18.20 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :135.00 Max. :150.000
## nr. tough sessions (effort in Z5, T1 or T2).2 nr. days with interval session.2
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9153 Mean :1.653
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.2 max km Z3-4 one day.2 total km Z5-T1-T2.2
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.000
## Mean : 4.793 Mean : 3.399 Mean : 3.977
## 3rd Qu.: 8.000 3rd Qu.: 6.200 3rd Qu.: 6.100
## Max. :85.000 Max. :75.000 Max. :52.200
## max km Z5-T1-T2 one day.2 total hours alternative training.2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.665 Mean : 1.204
## 3rd Qu.: 4.900 3rd Qu.: 1.580
## Max. :30.000 Max. :67.330
## nr. strength trainings.2 avg exertion.2 min exertion.2 max exertion.2
## Min. :0.0000 Min. :0.00 Min. :0.0000 Min. :0.00
## 1st Qu.:0.0000 1st Qu.:0.14 1st Qu.:0.0900 1st Qu.:0.16
## Median :1.0000 Median :0.32 Median :0.1400 Median :0.51
## Mean :0.8244 Mean :0.32 Mean :0.1878 Mean :0.47
## 3rd Qu.:1.0000 3rd Qu.:0.49 3rd Qu.:0.2700 3rd Qu.:0.73
## Max. :9.0000 Max. :0.98 Max. :0.9800 Max. :1.00
## avg training success.2 min training success.2 max training success.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3800 Median :0.7300
## Mean :0.4481 Mean :0.3396 Mean :0.5257
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.2 min recovery.2 max recovery.2 Athlete ID
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.00
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.:20.00
## Median :0.2200 Median :0.1600 Median :0.3100 Median :34.00
## Mean :0.2551 Mean :0.1849 Mean :0.3435 Mean :34.54
## 3rd Qu.:0.3600 3rd Qu.:0.2400 3rd Qu.:0.5200 3rd Qu.:50.00
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :73.00
## injury rel total kms week 0_1 rel total kms week 0_2
## Min. :0.00000 Min. : 0 Min. : 0
## 1st Qu.:0.00000 1st Qu.: 1 1st Qu.: 1
## Median :0.00000 Median : 1 Median : 1
## Mean :0.01344 Mean : 440863 Mean : 901468
## 3rd Qu.:0.00000 3rd Qu.: 1 3rd Qu.: 1
## Max. :1.00000 Max. :209600000 Max. :217600000
## rel total kms week 1_2 Date
## Min. : 0 Min. : 0
## 1st Qu.: 1 1st Qu.: 437
## Median : 1 Median :1254
## Mean : 480362 Mean :1228
## 3rd Qu.: 1 3rd Qu.:1913
## Max. :209600000 Max. :2673noninjured <-week[week$injury == 0,]
summary(noninjured)## nr. sessions nr. rest days total kms max km one day
## Min. : 0.000 Min. :0.000 Min. : 0.00 Min. : 0.0
## 1st Qu.: 5.000 1st Qu.:1.000 1st Qu.: 22.60 1st Qu.: 9.0
## Median : 6.000 Median :1.000 Median : 44.70 Median : 13.4
## Mean : 5.801 Mean :1.882 Mean : 49.51 Mean : 14.0
## 3rd Qu.: 7.000 3rd Qu.:3.000 3rd Qu.: 70.10 3rd Qu.: 18.3
## Max. :14.000 Max. :7.000 Max. :242.00 Max. :131.0
## total km Z3-Z4-Z5-T1-T2 nr. tough sessions (effort in Z5, T1 or T2)
## Min. : 0.000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000
## Median : 8.000 Median :1.0000
## Mean : 9.405 Mean :0.9272
## 3rd Qu.: 14.500 3rd Qu.:2.0000
## Max. :100.000 Max. :6.0000
## nr. days with interval session total km Z3-4 max km Z3-4 one day
## Min. :0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median :2.000 Median : 0.000 Median : 0.00
## Mean :1.668 Mean : 4.849 Mean : 3.45
## 3rd Qu.:3.000 3rd Qu.: 8.000 3rd Qu.: 6.30
## Max. :7.000 Max. :79.800 Max. :75.00
## total km Z5-T1-T2 max km Z5-T1-T2 one day total hours alternative training
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.500 Median : 1.000 Median : 0.000
## Mean : 4.049 Mean : 2.717 Mean : 1.149
## 3rd Qu.: 6.300 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :80.000 Max. :76.000 Max. :52.500
## nr. strength trainings avg exertion min exertion max exertion
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8123 Mean :0.3188 Mean :0.1882 Mean :0.4688
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :0.9800 Max. :0.9800 Max. :1.0000
## avg training success min training success max training success
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3700 Median :0.7300
## Mean :0.4461 Mean :0.3376 Mean :0.5233
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery min recovery max recovery nr. sessions.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2549 Mean :0.1854 Mean :0.3431 Mean : 5.802
## 3rd Qu.:0.3600 3rd Qu.:0.2500 3rd Qu.:0.5200 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.1 total kms.1 max km one day.1 total km Z3-Z4-Z5-T1-T2.1
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 22.10 1st Qu.: 8.80 1st Qu.: 0.800
## Median :1.000 Median : 44.40 Median :13.30 Median : 8.000
## Mean :1.884 Mean : 49.23 Mean :13.91 Mean : 9.367
## 3rd Qu.:3.000 3rd Qu.: 70.00 3rd Qu.:18.30 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :90.00 Max. :106.200
## nr. tough sessions (effort in Z5, T1 or T2).1 nr. days with interval session.1
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9225 Mean :1.661
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.1 max km Z3-4 one day.1 total km Z5-T1-T2.1
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.200
## Mean : 4.836 Mean : 3.434 Mean : 4.012
## 3rd Qu.: 8.000 3rd Qu.: 6.300 3rd Qu.: 6.200
## Max. :85.000 Max. :75.000 Max. :80.000
## max km Z5-T1-T2 one day.1 total hours alternative training.1
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.688 Mean : 1.173
## 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :76.000 Max. :52.500
## nr. strength trainings.1 avg exertion.1 min exertion.1 max exertion.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8148 Mean :0.3186 Mean :0.1876 Mean :0.4685
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg training success.1 min training success.1 max training success.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3800 Median :0.7300
## Mean :0.4468 Mean :0.3389 Mean :0.5237
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.1 min recovery.1 max recovery.1 nr. sessions.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2546 Mean :0.1849 Mean :0.3427 Mean : 5.807
## 3rd Qu.:0.3600 3rd Qu.:0.2400 3rd Qu.:0.5100 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.2 total kms.2 max km one day.2 total km Z3-Z4-Z5-T1-T2.2
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 21.60 1st Qu.: 8.60 1st Qu.: 0.000
## Median :1.000 Median : 43.90 Median : 13.20 Median : 7.900
## Mean :1.887 Mean : 48.82 Mean : 13.81 Mean : 9.291
## 3rd Qu.:3.000 3rd Qu.: 69.70 3rd Qu.: 18.20 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :135.00 Max. :150.000
## nr. tough sessions (effort in Z5, T1 or T2).2 nr. days with interval session.2
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9143 Mean :1.652
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.2 max km Z3-4 one day.2 total km Z5-T1-T2.2
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.000
## Mean : 4.793 Mean : 3.398 Mean : 3.973
## 3rd Qu.: 8.000 3rd Qu.: 6.200 3rd Qu.: 6.100
## Max. :85.000 Max. :75.000 Max. :52.200
## max km Z5-T1-T2 one day.2 total hours alternative training.2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.662 Mean : 1.204
## 3rd Qu.: 4.900 3rd Qu.: 1.580
## Max. :30.000 Max. :67.330
## nr. strength trainings.2 avg exertion.2 min exertion.2 max exertion.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8223 Mean :0.3191 Mean :0.1873 Mean :0.4686
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :0.9800 Max. :0.9800 Max. :1.0000
## avg training success.2 min training success.2 max training success.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3700 Median :0.7300
## Mean :0.4469 Mean :0.3388 Mean :0.5241
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.2 min recovery.2 max recovery.2 Athlete ID injury
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.00 Min. :0
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.:20.00 1st Qu.:0
## Median :0.2200 Median :0.1600 Median :0.3100 Median :34.00 Median :0
## Mean :0.2546 Mean :0.1847 Mean :0.3426 Mean :34.53 Mean :0
## 3rd Qu.:0.3600 3rd Qu.:0.2400 3rd Qu.:0.5100 3rd Qu.:50.00 3rd Qu.:0
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :73.00 Max. :0
## rel total kms week 0_1 rel total kms week 0_2 rel total kms week 1_2
## Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 1 1st Qu.: 1 1st Qu.: 1
## Median : 1 Median : 1 Median : 1
## Mean : 442719 Mean : 899340 Mean : 480452
## 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1
## Max. :209600000 Max. :217600000 Max. :209600000
## Date
## Min. : 0
## 1st Qu.: 429
## Median :1251
## Mean :1225
## 3rd Qu.:1909
## Max. :2652injured <- week[week$injury == 1, ]
summary(injured)## nr. sessions nr. rest days total kms max km one day
## Min. : 0.000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 5.000 1st Qu.:0.000 1st Qu.: 30.70 1st Qu.:11.00
## Median : 6.000 Median :1.000 Median : 49.00 Median :14.30
## Mean : 6.435 Mean :1.353 Mean : 51.86 Mean :14.77
## 3rd Qu.: 8.000 3rd Qu.:2.000 3rd Qu.: 69.00 3rd Qu.:18.00
## Max. :13.000 Max. :7.000 Max. :174.00 Max. :57.00
## total km Z3-Z4-Z5-T1-T2 nr. tough sessions (effort in Z5, T1 or T2)
## Min. : 0.00 Min. :0.000
## 1st Qu.: 5.90 1st Qu.:0.000
## Median :10.90 Median :1.000
## Mean :11.55 Mean :1.146
## 3rd Qu.:16.50 3rd Qu.:2.000
## Max. :45.50 Max. :4.000
## nr. days with interval session total km Z3-4 max km Z3-4 one day
## Min. :0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median :2.000 Median : 3.800 Median : 3.500
## Mean :1.991 Mean : 5.593 Mean : 3.939
## 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :6.000 Max. :35.400 Max. :21.100
## total km Z5-T1-T2 max km Z5-T1-T2 one day total hours alternative training
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 4.000 Median : 3.600 Median : 0.000
## Mean : 5.179 Mean : 3.358 Mean : 1.142
## 3rd Qu.: 8.150 3rd Qu.: 6.000 3rd Qu.: 1.500
## Max. :32.300 Max. :15.000 Max. :27.420
## nr. strength trainings avg exertion min exertion max exertion
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.2400 1st Qu.:0.1000 1st Qu.:0.4100
## Median :1.000 Median :0.4300 Median :0.1800 Median :0.6900
## Mean :1.061 Mean :0.4043 Mean :0.2213 Mean :0.6029
## 3rd Qu.:2.000 3rd Qu.:0.5600 3rd Qu.:0.3100 3rd Qu.:0.8050
## Max. :5.000 Max. :0.8500 Max. :0.7000 Max. :1.0000
## avg training success min training success max training success
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.5100 1st Qu.:0.1450 1st Qu.:0.6500
## Median :0.6600 Median :0.4600 Median :0.7800
## Mean :0.5507 Mean :0.3977 Mean :0.6539
## 3rd Qu.:0.7400 3rd Qu.:0.6200 3rd Qu.:0.8700
## Max. :0.9800 Max. :0.9600 Max. :1.0000
## avg recovery min recovery max recovery nr. sessions.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1800 1st Qu.:0.1200 1st Qu.:0.2150 1st Qu.: 5.000
## Median :0.2800 Median :0.1800 Median :0.4100 Median : 6.000
## Mean :0.2997 Mean :0.2031 Mean :0.4254 Mean : 6.294
## 3rd Qu.:0.4000 3rd Qu.:0.2700 3rd Qu.:0.5900 3rd Qu.: 8.000
## Max. :0.8600 Max. :0.7000 Max. :1.0000 Max. :14.000
## nr. rest days.1 total kms.1 max km one day.1 total km Z3-Z4-Z5-T1-T2.1
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:1.000 1st Qu.: 30.55 1st Qu.: 10.50 1st Qu.: 4.80
## Median :1.000 Median : 47.30 Median : 14.00 Median :10.00
## Mean :1.482 Mean : 51.19 Mean : 14.73 Mean :10.65
## 3rd Qu.:2.000 3rd Qu.: 66.85 3rd Qu.: 18.00 3rd Qu.:15.50
## Max. :7.000 Max. :202.00 Max. :130.00 Max. :54.60
## nr. tough sessions (effort in Z5, T1 or T2).1 nr. days with interval session.1
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.000
## Median :1.000 Median :2.000
## Mean :1.087 Mean :1.875
## 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :4.000 Max. :5.000
## total km Z3-4.1 max km Z3-4 one day.1 total km Z5-T1-T2.1
## Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 2.50 Median : 2.400 Median : 3.200
## Mean : 5.22 Mean : 3.705 Mean : 4.732
## 3rd Qu.: 8.50 3rd Qu.: 6.500 3rd Qu.: 7.850
## Max. :34.70 Max. :21.100 Max. :42.100
## max km Z5-T1-T2 one day.1 total hours alternative training.1
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 3.000 Median : 0.000
## Mean : 3.075 Mean : 1.057
## 3rd Qu.: 5.600 3rd Qu.: 1.500
## Max. :21.100 Max. :12.000
## nr. strength trainings.1 avg exertion.1 min exertion.1 max exertion.1
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.2150 1st Qu.:0.1100 1st Qu.:0.3400
## Median :1.000 Median :0.4200 Median :0.1800 Median :0.6800
## Mean :1.071 Mean :0.3946 Mean :0.2211 Mean :0.5871
## 3rd Qu.:2.000 3rd Qu.:0.5400 3rd Qu.:0.3100 3rd Qu.:0.8000
## Max. :5.000 Max. :0.9100 Max. :0.8800 Max. :1.0000
## avg training success.1 min training success.1 max training success.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4800 1st Qu.:0.1350 1st Qu.:0.6350
## Median :0.6500 Median :0.4600 Median :0.7800
## Mean :0.5454 Mean :0.4016 Mean :0.6457
## 3rd Qu.:0.7400 3rd Qu.:0.6200 3rd Qu.:0.8600
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.1 min recovery.1 max recovery.1 nr. sessions.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.00
## 1st Qu.:0.1700 1st Qu.:0.1300 1st Qu.:0.2000 1st Qu.: 5.00
## Median :0.2800 Median :0.1800 Median :0.3800 Median : 6.00
## Mean :0.2953 Mean :0.2029 Mean :0.4123 Mean : 6.07
## 3rd Qu.:0.4000 3rd Qu.:0.2700 3rd Qu.:0.5900 3rd Qu.: 7.00
## Max. :0.7300 Max. :0.6200 Max. :1.0000 Max. :14.00
## nr. rest days.2 total kms.2 max km one day.2 total km Z3-Z4-Z5-T1-T2.2
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 26.90 1st Qu.: 9.75 1st Qu.: 3.000
## Median :1.000 Median : 42.50 Median :13.40 Median : 9.000
## Mean :1.652 Mean : 47.69 Mean :13.91 Mean : 9.737
## 3rd Qu.:2.000 3rd Qu.: 66.80 3rd Qu.:17.75 3rd Qu.:14.750
## Max. :7.000 Max. :191.00 Max. :52.90 Max. :59.800
## nr. tough sessions (effort in Z5, T1 or T2).2 nr. days with interval session.2
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:1.000
## Median :1.0000 Median :2.000
## Mean :0.9913 Mean :1.729
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :4.0000 Max. :5.000
## total km Z3-4.2 max km Z3-4 one day.2 total km Z5-T1-T2.2
## Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.000 Median : 0.00 Median : 3.000
## Mean : 4.818 Mean : 3.48 Mean : 4.325
## 3rd Qu.: 8.000 3rd Qu.: 6.50 3rd Qu.: 6.550
## Max. :39.000 Max. :30.00 Max. :30.000
## max km Z5-T1-T2 one day.2 total hours alternative training.2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 2.500 Median : 0.000
## Mean : 2.881 Mean : 1.212
## 3rd Qu.: 5.000 3rd Qu.: 1.615
## Max. :20.000 Max. :21.780
## nr. strength trainings.2 avg exertion.2 min exertion.2 max exertion.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1800 1st Qu.:0.1000 1st Qu.:0.2800
## Median :1.0000 Median :0.4200 Median :0.1900 Median :0.6800
## Mean :0.9791 Mean :0.3873 Mean :0.2203 Mean :0.5748
## 3rd Qu.:2.0000 3rd Qu.:0.5500 3rd Qu.:0.3200 3rd Qu.:0.7900
## Max. :6.0000 Max. :0.7800 Max. :0.7300 Max. :1.0000
## avg training success.2 min training success.2 max training success.2
## Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.480 1st Qu.:0.115 1st Qu.:0.6300
## Median :0.650 Median :0.470 Median :0.7900
## Mean :0.541 Mean :0.397 Mean :0.6412
## 3rd Qu.:0.740 3rd Qu.:0.620 3rd Qu.:0.8700
## Max. :1.000 Max. :1.000 Max. :1.0000
## avg recovery.2 min recovery.2 max recovery.2 Athlete ID injury
## Min. :0.0000 Min. :0.00 Min. :0.0000 Min. : 0.00 Min. :1
## 1st Qu.:0.1700 1st Qu.:0.12 1st Qu.:0.1950 1st Qu.:23.00 1st Qu.:1
## Median :0.2800 Median :0.17 Median :0.4000 Median :36.00 Median :1
## Mean :0.2916 Mean :0.20 Mean :0.4076 Mean :35.42 Mean :1
## 3rd Qu.:0.4000 3rd Qu.:0.27 3rd Qu.:0.5700 3rd Qu.:50.00 3rd Qu.:1
## Max. :0.7500 Max. :0.69 Max. :1.0000 Max. :71.00 Max. :1
## rel total kms week 0_1 rel total kms week 0_2 rel total kms week 1_2
## Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 1 1st Qu.: 1 1st Qu.: 1
## Median : 1 Median : 1 Median : 1
## Mean : 304523 Mean : 1057740 Mean : 473740
## 3rd Qu.: 1 3rd Qu.: 2 3rd Qu.: 1
## Max. :47000000 Max. :100200000 Max. :53100000
## Date
## Min. : 246.0
## 1st Qu.: 765.5
## Median :1485.0
## Mean :1431.6
## 3rd Qu.:2075.5
## Max. :2673.0injured$injury = (as.factor(injured$injury))
levels(injured$injury) <- c("Injured")
noninjured$injury = (as.factor(noninjured$injury))
levels(noninjured$injury) <- c("NotInjured")injury_dist <- week %>%
group_by(`Athlete ID`, injury) %>%
summarise(count = n()) %>%
spread(injury, count, fill = 0) ## `summarise()` has grouped output by 'Athlete ID'. You can override using the
## `.groups` argument.injury_dist## # A tibble: 74 × 3
## # Groups: Athlete ID [74]
## `Athlete ID` `0` `1`
## <dbl> <dbl> <dbl>
## 1 0 303 7
## 2 1 459 2
## 3 2 1287 4
## 4 3 341 3
## 5 4 674 7
## 6 5 201 1
## 7 6 530 4
## 8 7 299 5
## 9 8 261 7
## 10 9 1210 22
## # … with 64 more rowscolnames(injury_dist) <- c("Athlete ID", "Not Injured", "Injured")
#p <- ggplot(injury_dist, aes(x=`Athlete ID`, fill=Injured))+ # Set dataset and aesthetics
# geom_density(alpha = 0.3, fill='red3')
#pinjury_sum <- week %>%
subset(week$injury==1,)
injury_sum <- injury_sum %>%
group_by(`Athlete ID`, injury) %>%
mutate(Freq = n())
injury_sum## # A tibble: 575 × 73
## # Groups: Athlete ID, injury [61]
## nr. session…¹ nr. r…² total…³ max k…⁴ total…⁵ nr. t…⁶ nr. d…⁷ total…⁸ max k…⁹
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 6 1 34.9 14.5 10.4 0 2 9.5 5
## 2 6 1 33.6 15.2 15.3 1 2 12.4 6.4
## 3 1 6 7 7 0 0 0 0 0
## 4 7 0 47.2 12.5 14.7 0 3 14 6
## 5 9 1 29.4 7.5 3.9 3 3 0 0
## 6 7 0 28.5 6.9 10.8 1 4 10 4.5
## 7 3 4 23.4 12 1.9 1 2 1.5 1.5
## 8 6 1 117 22 20 0 2 20 11
## 9 6 1 114 30 10 1 1 0 0
## 10 6 2 78 24 15 1 2 9 9
## # … with 565 more rows, 64 more variables: `total km Z5-T1-T2` <dbl>,
## # `max km Z5-T1-T2 one day` <dbl>, `total hours alternative training` <dbl>,
## # `nr. strength trainings` <dbl>, `avg exertion` <dbl>, `min exertion` <dbl>,
## # `max exertion` <dbl>, `avg training success` <dbl>,
## # `min training success` <dbl>, `max training success` <dbl>,
## # `avg recovery` <dbl>, `min recovery` <dbl>, `max recovery` <dbl>,
## # `nr. sessions.1` <dbl>, `nr. rest days.1` <dbl>, `total kms.1` <dbl>, …ggplot(injury_sum, aes(x=`Athlete ID`, fill=Freq))+
geom_bar()## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Create plot
p <- ggplot(injury_sum, aes(x = `max exertion`, fill = injury)) + # Set dataset and aesthetics
geom_density(alpha = 0.3, fill='red3') +
labs(x = "Max Exertion", y = "Injuries", # Set labels for plot
title = "Injuries by Max Exertion") +
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
dark_theme_bw() # Turn theme to dark mode## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().p1 <- ggplot(injury_sum, aes(x = `avg recovery`, fill = injury)) + # Set dataset and aesthetics
geom_density(alpha = 0.3, fill ="blue2") +
labs(x = "Average Recovery", y = "Injuries", # Set labels for plot
title = "Injuries by Average Recovery") +
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
dark_theme_bw() # Turn theme to dark mode
p <- ggarrange(p, p1, ncol = 2, widths = c(3,3), common.legend = TRUE, legend = "bottom")
print(p)
For Weeks that an Athlete was Injured this was their average exertion
# Create a ggplot boxplot
p <- ggplot(week, aes(x = injury, y = `avg exertion`, fill=injury)) +
geom_boxplot()
p## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Create an interactive plotly version of the ggplot
ggplotly(p) %>%
layout(
title = "Boxplot of Average Exertion by Injury Status",
xaxis = list(title = "Injury Status"),
yaxis = list(title = "Average Exertion")
)## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?p <- week %>%
ggplot(aes(x = Date)) +
geom_density(aes( fill = injury), alpha = 0.3) +
scale_fill_manual(values = c("red2", "green")) +
labs(title = "Bar Chart and Density Plot of Average Exertion by Injury Status",
x = "Date", y = "Density", fill = "Injury Status")
p## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
summary(week)## nr. sessions nr. rest days total kms max km one day
## Min. : 0.000 Min. :0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 5.000 1st Qu.:1.000 1st Qu.: 22.80 1st Qu.: 9.00
## Median : 6.000 Median :1.000 Median : 44.80 Median : 13.40
## Mean : 5.809 Mean :1.875 Mean : 49.54 Mean : 14.01
## 3rd Qu.: 7.000 3rd Qu.:3.000 3rd Qu.: 70.10 3rd Qu.: 18.30
## Max. :14.000 Max. :7.000 Max. :242.00 Max. :131.00
## total km Z3-Z4-Z5-T1-T2 nr. tough sessions (effort in Z5, T1 or T2)
## Min. : 0.000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000
## Median : 8.000 Median :1.0000
## Mean : 9.434 Mean :0.9302
## 3rd Qu.: 14.600 3rd Qu.:2.0000
## Max. :100.000 Max. :6.0000
## nr. days with interval session total km Z3-4 max km Z3-4 one day
## Min. :0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median :2.000 Median : 0.000 Median : 0.000
## Mean :1.673 Mean : 4.859 Mean : 3.457
## 3rd Qu.:3.000 3rd Qu.: 8.000 3rd Qu.: 6.300
## Max. :7.000 Max. :79.800 Max. :75.000
## total km Z5-T1-T2 max km Z5-T1-T2 one day total hours alternative training
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.500 Median : 1.200 Median : 0.000
## Mean : 4.064 Mean : 2.725 Mean : 1.149
## 3rd Qu.: 6.300 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :80.000 Max. :76.000 Max. :52.500
## nr. strength trainings avg exertion min exertion max exertion
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8156 Mean :0.3199 Mean :0.1887 Mean :0.4706
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :0.9800 Max. :0.9800 Max. :1.0000
## avg training success min training success max training success
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3700 Median :0.7300
## Mean :0.4475 Mean :0.3384 Mean :0.5251
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery min recovery max recovery nr. sessions.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2555 Mean :0.1856 Mean :0.3442 Mean : 5.808
## 3rd Qu.:0.3600 3rd Qu.:0.2500 3rd Qu.:0.5200 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.1 total kms.1 max km one day.1 total km Z3-Z4-Z5-T1-T2.1
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 22.20 1st Qu.: 8.80 1st Qu.: 0.800
## Median :1.000 Median : 44.40 Median : 13.30 Median : 8.000
## Mean :1.879 Mean : 49.26 Mean : 13.92 Mean : 9.384
## 3rd Qu.:3.000 3rd Qu.: 70.00 3rd Qu.: 18.30 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :130.00 Max. :106.200
## nr. tough sessions (effort in Z5, T1 or T2).1 nr. days with interval session.1
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9247 Mean :1.664
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.1 max km Z3-4 one day.1 total km Z5-T1-T2.1
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.400
## Mean : 4.841 Mean : 3.438 Mean : 4.022
## 3rd Qu.: 8.000 3rd Qu.: 6.300 3rd Qu.: 6.200
## Max. :85.000 Max. :75.000 Max. :80.000
## max km Z5-T1-T2 one day.1 total hours alternative training.1
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.693 Mean : 1.172
## 3rd Qu.: 5.000 3rd Qu.: 1.500
## Max. :76.000 Max. :52.500
## nr. strength trainings.1 avg exertion.1 min exertion.1 max exertion.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.1400 1st Qu.:0.0900 1st Qu.:0.1600
## Median :1.0000 Median :0.3200 Median :0.1400 Median :0.5100
## Mean :0.8182 Mean :0.3196 Mean :0.1881 Mean :0.4701
## 3rd Qu.:1.0000 3rd Qu.:0.4800 3rd Qu.:0.2700 3rd Qu.:0.7300
## Max. :9.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg training success.1 min training success.1 max training success.1
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3800 Median :0.7300
## Mean :0.4481 Mean :0.3398 Mean :0.5254
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.1 min recovery.1 max recovery.1 nr. sessions.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.: 5.000
## Median :0.2200 Median :0.1600 Median :0.3100 Median : 6.000
## Mean :0.2551 Mean :0.1851 Mean :0.3436 Mean : 5.811
## 3rd Qu.:0.3600 3rd Qu.:0.2500 3rd Qu.:0.5200 3rd Qu.: 7.000
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :14.000
## nr. rest days.2 total kms.2 max km one day.2 total km Z3-Z4-Z5-T1-T2.2
## Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 21.70 1st Qu.: 8.60 1st Qu.: 0.200
## Median :1.000 Median : 43.90 Median : 13.20 Median : 7.900
## Mean :1.884 Mean : 48.81 Mean : 13.82 Mean : 9.297
## 3rd Qu.:3.000 3rd Qu.: 69.60 3rd Qu.: 18.20 3rd Qu.: 14.500
## Max. :7.000 Max. :235.00 Max. :135.00 Max. :150.000
## nr. tough sessions (effort in Z5, T1 or T2).2 nr. days with interval session.2
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :2.000
## Mean :0.9153 Mean :1.653
## 3rd Qu.:2.0000 3rd Qu.:3.000
## Max. :6.0000 Max. :7.000
## total km Z3-4.2 max km Z3-4 one day.2 total km Z5-T1-T2.2
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.000 Median : 0.000 Median : 1.000
## Mean : 4.793 Mean : 3.399 Mean : 3.977
## 3rd Qu.: 8.000 3rd Qu.: 6.200 3rd Qu.: 6.100
## Max. :85.000 Max. :75.000 Max. :52.200
## max km Z5-T1-T2 one day.2 total hours alternative training.2
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 0.000
## Mean : 2.665 Mean : 1.204
## 3rd Qu.: 4.900 3rd Qu.: 1.580
## Max. :30.000 Max. :67.330
## nr. strength trainings.2 avg exertion.2 min exertion.2 max exertion.2
## Min. :0.0000 Min. :0.00 Min. :0.0000 Min. :0.00
## 1st Qu.:0.0000 1st Qu.:0.14 1st Qu.:0.0900 1st Qu.:0.16
## Median :1.0000 Median :0.32 Median :0.1400 Median :0.51
## Mean :0.8244 Mean :0.32 Mean :0.1878 Mean :0.47
## 3rd Qu.:1.0000 3rd Qu.:0.49 3rd Qu.:0.2700 3rd Qu.:0.73
## Max. :9.0000 Max. :0.98 Max. :0.9800 Max. :1.00
## avg training success.2 min training success.2 max training success.2
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.5900 Median :0.3800 Median :0.7300
## Mean :0.4481 Mean :0.3396 Mean :0.5257
## 3rd Qu.:0.7300 3rd Qu.:0.6100 3rd Qu.:0.8400
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## avg recovery.2 min recovery.2 max recovery.2 Athlete ID
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.00
## 1st Qu.:0.1500 1st Qu.:0.1100 1st Qu.:0.1700 1st Qu.:20.00
## Median :0.2200 Median :0.1600 Median :0.3100 Median :34.00
## Mean :0.2551 Mean :0.1849 Mean :0.3435 Mean :34.54
## 3rd Qu.:0.3600 3rd Qu.:0.2400 3rd Qu.:0.5200 3rd Qu.:50.00
## Max. :0.9000 Max. :0.9000 Max. :1.0000 Max. :73.00
## injury rel total kms week 0_1 rel total kms week 0_2
## Min. :0.00000 Min. : 0 Min. : 0
## 1st Qu.:0.00000 1st Qu.: 1 1st Qu.: 1
## Median :0.00000 Median : 1 Median : 1
## Mean :0.01344 Mean : 440863 Mean : 901468
## 3rd Qu.:0.00000 3rd Qu.: 1 3rd Qu.: 1
## Max. :1.00000 Max. :209600000 Max. :217600000
## rel total kms week 1_2 Date
## Min. : 0 Min. : 0
## 1st Qu.: 1 1st Qu.: 437
## Median : 1 Median :1254
## Mean : 480362 Mean :1228
## 3rd Qu.: 1 3rd Qu.:1913
## Max. :209600000 Max. :2673# distance sum over 3 weeks
# week 0-2 (Distance in week 0 and the average across 1 and 2)ac_7_21 <- week$`total kms`/((week$`total kms.1` + week$`total kms.2`)/2)
ac_7_21[is.infinite(ac_7_21)] <- NAweek$ac_7_21 <- ac_7_21
p <- week %>%
ggplot(aes(x = ac_7_21)) +
geom_density(aes( fill = factor(injury)), alpha = 0.3) +
scale_fill_manual(values = c("red2", "green")) +
labs(title = "Bar Chart and Density Plot of Average Exertion by Injury Status",
x = "Date", y = "Density", fill = "Injury Status") +
xlim(0,3)
p## Warning: Removed 4726 rows containing non-finite values (`stat_density()`).
Derived Variables
ae_7_21 <- week$`avg exertion`/((week$`avg exertion.1` + week$`avg exertion.2`)/2)
ae_7_21[is.infinite(ae_7_21)] <- NA
exer_v_rec_7_21 <- week$`max exertion`/((week$`avg recovery.1`+ week$`avg recovery.2`)/2) ## Exertion 0 / recovery 1 over 3 weeks
exer_v_rec_7_21[is.infinite(exer_v_rec_7_21)] <- NA
er_7_21 <- week$`avg exertion`/((week$`avg recovery.1` + week$`avg recovery.2`)/2)
er_7_21[is.infinite(er_7_21)] <- NA
rec_maxexer_7_21<- week$`avg recovery`/((week$`max exertion.1` + week$`max exertion.2`)/2)
rec_maxexer_7_21[is.infinite(rec_maxexer_7_21)] <- NA
week$ae_7_21 <- ae_7_21
threshold <- 2.25
week$ae_7_21 <- ifelse(week$ae_7_21 > threshold, mean(week$ae_7_21), week$ae_7_21)
summary(week$ae_7_21)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.8966 1.0000 0.9989 1.1034 2.2500 2857week$exer_v_rec_7_21 <- exer_v_rec_7_21
threshold <- 2.25
week$exer_v_rec_7_21 <- ifelse(week$exer_v_rec_7_21 > threshold, mean(week$exer_v_rec_7_21), week$exer_v_rec_7_21)
summary(week$exer_v_rec_7_21)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.032 1.308 1.313 1.636 2.250 13929week$er_7_21 <- er_7_21
threshold <- 2.25
week$er_7_21 <- ifelse(week$er_7_21 > threshold, mean(week$er_7_21), week$er_7_21)
summary(week$er_7_21)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.875 1.080 1.125 1.368 2.250 6143week$rec_maxexer_7_21 <- rec_maxexer_7_21
threshold <- 2.25
week$rec_maxexer_7_21 <- ifelse(week$rec_maxexer_7_21 > threshold, mean(week$rec_maxexer_7_21), week$rec_maxexer_7_21)
summary(week$rec_maxexer_7_21)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.4051 0.6282 0.6550 0.8485 2.2500 2681model <- week[,c(1:3, 12:14, 16, 17, 20, 68, 73:76)]
model <- na.omit(model)
full_model <- glm(injury ~ ., data = model, family = binomial())
summary(full_model)##
## Call:
## glm(formula = injury ~ ., family = binomial(), data = model)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3103 -0.1743 -0.1444 -0.1230 3.5424
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.58411 0.57505 -6.233 4.59e-10 ***
## `nr. sessions` -0.05758 0.06873 -0.838 0.402170
## `nr. rest days` -0.28656 0.08330 -3.440 0.000582 ***
## `total kms` -0.00437 0.00265 -1.649 0.099152 .
## `total hours alternative training` -0.11027 0.04085 -2.699 0.006948 **
## `nr. strength trainings` 0.06048 0.06811 0.888 0.374496
## `avg exertion` 2.01601 2.43478 0.828 0.407667
## `max exertion` 0.55473 1.79439 0.309 0.757208
## `avg training success` -0.23085 0.31246 -0.739 0.460019
## `avg recovery` -0.81802 0.77478 -1.056 0.291061
## ac_7_21 -0.03031 0.06297 -0.481 0.630315
## ae_7_21 0.07275 0.33287 0.219 0.827005
## exer_v_rec_7_21 0.23017 0.71383 0.322 0.747112
## er_7_21 -0.67293 0.95695 -0.703 0.481931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3558.7 on 27553 degrees of freedom
## Residual deviance: 3478.9 on 27540 degrees of freedom
## AIC: 3506.9
##
## Number of Fisher Scoring iterations: 7step_model <- stepAIC(full_model, direction = "forward", scope = list(lower = ~1, upper = ~.), trace = FALSE)
summary(step_model)##
## Call:
## glm(formula = injury ~ `nr. sessions` + `nr. rest days` + `total kms` +
## `total hours alternative training` + `nr. strength trainings` +
## `avg exertion` + `max exertion` + `avg training success` +
## `avg recovery` + ac_7_21 + ae_7_21 + exer_v_rec_7_21 + er_7_21,
## family = binomial(), data = model)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3103 -0.1743 -0.1444 -0.1230 3.5424
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.58411 0.57505 -6.233 4.59e-10 ***
## `nr. sessions` -0.05758 0.06873 -0.838 0.402170
## `nr. rest days` -0.28656 0.08330 -3.440 0.000582 ***
## `total kms` -0.00437 0.00265 -1.649 0.099152 .
## `total hours alternative training` -0.11027 0.04085 -2.699 0.006948 **
## `nr. strength trainings` 0.06048 0.06811 0.888 0.374496
## `avg exertion` 2.01601 2.43478 0.828 0.407667
## `max exertion` 0.55473 1.79439 0.309 0.757208
## `avg training success` -0.23085 0.31246 -0.739 0.460019
## `avg recovery` -0.81802 0.77478 -1.056 0.291061
## ac_7_21 -0.03031 0.06297 -0.481 0.630315
## ae_7_21 0.07275 0.33287 0.219 0.827005
## exer_v_rec_7_21 0.23017 0.71383 0.322 0.747112
## er_7_21 -0.67293 0.95695 -0.703 0.481931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3558.7 on 27553 degrees of freedom
## Residual deviance: 3478.9 on 27540 degrees of freedom
## AIC: 3506.9
##
## Number of Fisher Scoring iterations: 7New Analysis
Lets investigate injuries as a part of our data set
Looks as if our current percentage of injuries as opposed to training days is 1.42 %
training_days <- sum(week$`nr. sessions` > 0)
sum(as.numeric(week$injury)) / training_days## [1] 0.01419087summary(as.factor(week$injury))## 0 1
## 42223 575athlete <- week %>%
group_by(`Athlete ID`) %>%
summarise(Injuries = sum(as.numeric(injury))) %>%
arrange(desc(Injuries)) %>%
head(10)
athlete## # A tibble: 10 × 2
## `Athlete ID` Injuries
## <dbl> <dbl>
## 1 26 35
## 2 29 33
## 3 42 32
## 4 23 24
## 5 9 22
## 6 27 22
## 7 36 22
## 8 22 21
## 9 38 18
## 10 41 18Injury_Plot <- ggplot(athlete, mapping = aes(x = Injuries)) +
geom_density(alpha=0.5, fill="maroon") +
labs(x = "Total Injuries",
title = "Distribution of Injuries among Athletes")
Injury_Plot
Lets focus on the most injured athlete
athlete1 <- week[week$`Athlete ID` == "26",]
# Create plot
Athlete_Workloads <- ggplot(athlete1, # Set data
aes(x = `Date`, y = `avg exertion`, color = factor(injury))) + # Set aesthetics
geom_point(alpha = 0.5) + # Set geom_point for scatter plot
labs(x = "Days", # Set labels
title = "Athlete 26 Injuries",
fill = "Injuries") +
theme_bw() +
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
scale_color_manual(values = c("0" = "blue", "1" = "red"), # Set color values
labels = c("0" = "Healthy", "1" = "Injured"))
# Generate graph
ggMarginal(Athlete_Workloads , groupFill = TRUE,
type = "histogram")
pivotdat <- pivot_longer(athlete1[, c("min recovery", "avg recovery", "max recovery", "min exertion", "avg exertion", "max exertion", "min training success", "avg training success", "max training success", "Athlete ID", "injury")],
!c("Athlete ID", "injury")) # Set variables to use as ID
pivotdatall <- pivot_longer(week[, c("min recovery", "avg recovery", "max recovery", "min exertion", "avg exertion", "max exertion", "min training success", "avg training success", "max training success", "Athlete ID", "injury")],
!c("Athlete ID", "injury")) # Set variables to use as IDg <- ggplot(pivotdat,
aes(x = value, fill = factor(injury))) + # Set aesthetics
geom_density(alpha = 0.3) + # Set geom density for density plot
labs(x = "Percieved Metric Values", # Set labels
title = "Injuries v Percieved Metric Values Leading Up to Injury",
fill = "injury") +
facet_wrap(~name, scale = "free") +
theme_bw() + # Set theme
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
scale_fill_manual(values = c("0" = "blue", "1" = "red"), # Set color values
labels = c("0" = "Healthy", "1" = "Injury"))
# Generate graph
g
gall <- ggplot(pivotdatall,
aes(x = value, fill = factor(injury))) + # Set aesthetics
geom_density(alpha = 0.3) + # Set geom density for density plot
labs(x = "Percieved Metric Values", # Set labels
title = "Injuries v Percieved Metric Values Leading Up to Injury",
fill = "injury") +
facet_wrap(~name, scale = "free") +
theme_bw() + # Set theme
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
scale_fill_manual(values = c("0" = "blue", "1" = "red"), # Set color values
labels = c("0" = "Healthy", "1" = "Injury"))
# Generate graph
gall
Athlete ID 26 Week before injury
# Lets select top 10 most injured athletes to see if there are any outstanding similarities
athletes <- week[week$`Athlete ID` %in% athlete$`Athlete ID`,]
# Create graph
athlete_ex <- ggplot(athlete1, # Set data
aes(x = `avg recovery.1`, y = `max exertion` , color = factor(injury))) + # Set aesthetics
geom_point(alpha = 0.3) + # Set geom point for scatter plot
labs(x = "Avg Recovery week before Injury", # Set labels
y = "Max Exertion week of Injury",
title = "Max Exertion versus Average Recovery before injury",
fill = "injury") +
theme_bw() + # Set theme
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
scale_color_manual(values = c("0" = "blue", "1" = "red"), # Set color values
labels = c("0" = "Healthy", "1" = "Injury"))
# Generate graph
ggMarginal(athlete_ex, groupFill = TRUE)
# Create graph
week_ex <- ggplot(week, # Set data
aes(x = `avg recovery.1`, y = `max exertion`,color = factor(injury))) + # Set aesthetics
geom_point(alpha = 0.3) + # Set geom point for scatter plot
labs(x = "Avg Recovery week before Injury", # Set labels
y = "Max Exertion week of Injury",
title = "Max Exertion versus Average Recovery before injury",
fill = "injury") +
theme_bw() + # Set theme
theme(axis.line = element_line(colour = "black"), # Set axis line as black
panel.grid.major = element_blank(), # Remove grid
panel.grid.minor = element_blank(), # Remove grid
panel.border = element_blank(), # Remove grid
panel.background = element_blank()) + # Remove grid
scale_color_manual(values = c("0" = "blue", "1" = "red"), # Set color values
labels = c("0" = "Healthy", "1" = "Injury"))
# Generate graph
ggMarginal(week_ex, groupFill = TRUE)
set.seed(42069) # Set seed for reproducibility
trainIndex <- createDataPartition(y = week$injury, p = 0.75, list = FALSE)
trainData <- week[trainIndex, ]
testData <- week[-trainIndex, ]
validData <- week[week$injury==1,]
# c(1:4,6, 12:22, 73:76)
# Create training data
dtrain_1 <- xgb.DMatrix(data = as.matrix(trainData[,c(1:4,6, 12:22, 36:44, 58:66, 74:77)]), label = ((trainData$injury)))
# Create test data
dtest_1 <- xgb.DMatrix(data = as.matrix(testData[,c(1:4,6, 12:22, 36:44, 58:66, 74:77)]), label = ((testData$injury)))fit_1 <- xgboost(dtrain_1, # Set data set to use
nrounds = 200, # Set number of rounds
eta = 0.05 ,
verbose = 1, # 1 - Prints out fit
print_every_n = 20, # Prints out result every 20th iteration
objective = "binary:logistic", # Set objective
eval_metric = "auc",
eval_metric = "error")## [1] train-auc:0.611286 train-error:0.013303
## [21] train-auc:0.627602 train-error:0.013178
## [41] train-auc:0.703593 train-error:0.013209
## [61] train-auc:0.799753 train-error:0.013240
## [81] train-auc:0.866385 train-error:0.013271
## [101] train-auc:0.917969 train-error:0.013240
## [121] train-auc:0.939627 train-error:0.013085
## [141] train-auc:0.949698 train-error:0.012929
## [161] train-auc:0.960221 train-error:0.012742
## [181] train-auc:0.967799 train-error:0.012493
## [200] train-auc:0.974267 train-error:0.012430preds_injury <- predict(fit_1, dtest_1)
roc1 = roc((testData$injury), preds_injury)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot.roc(roc1, print.auc = TRUE, col = "red", print.auc.col = "red")
pred_class <- rep(0, length(preds_injury))
# If predicted probability above cutoff set as 1
pred_class[which(preds_injury > 0.0145)] <- 1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 7946 61
## 1 2605 87
##
## Accuracy : 0.7508
## 95% CI : (0.7425, 0.759)
## No Information Rate : 0.9862
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.036
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.75310
## Specificity : 0.58784
## Pos Pred Value : 0.99238
## Neg Pred Value : 0.03232
## Prevalence : 0.98617
## Detection Rate : 0.74269
## Detection Prevalence : 0.74839
## Balanced Accuracy : 0.67047
##
## 'Positive' Class : 0
## shap_result_1 <- shap.score.rank(xgb_model = fit_1,
X_train = as.matrix(trainData[,c(1:4,6, 12:22, 36:44, 58:66, 74:77)]),
shap_approx = F)## Loading required package: data.table##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## make SHAP score by decreasing ordershap_long_1 = shap.prep(shap = shap_result_1,
X_train = as.matrix(trainData[,c(1:4,6, 12:22, 36:44, 58:66, 74:77)]),
top_n = 8)## Loading required package: ggforceplot.shap.summary(data_long = shap_long_1)
Future Steps Could possibly redo study to focus on the injuries of the top 10 most injured runners to see if they have any signficant metrics that lead to injury.
athletes <- week[week$`Athlete ID` %in% athlete$`Athlete ID`,]